DataMaker MCP Server

The Automators DataMaker MCP (Model Context Protocol) server provides a seamless integration between DataMaker and the Model Context Protocol, enabling AI models to interact with DataMaker's powerful data generation capabilities.

🚀 Features

• Generate synthetic data using DataMaker templates

• Fetch and manage DataMaker templates

• Fetch and manage DataMaker connections

• Push data to DataMaker connections

• Large dataset handling: Automatically stores large endpoint datasets to S3 and provides summary with view links

• Execute Python scripts: Dynamically execute Python code by saving scripts to S3 and running them using the DataMaker runner

Related MCP server: Databricks MCP Server

📦 Installation

Add the following to your mcp.json file:

{
  "mcpServers": {
    "datamaker": {
      "command": "npx",
      "args": ["-y", "@automators/datamaker-mcp"],
      "env": {
        "DATAMAKER_API_KEY": "your-datamaker-api-key"
      }
    }
  }
}

📋 Prerequisites

• Node.js (LTS version recommended)

• pnpm package manager (v10.5.2 or later)

• A DataMaker account with API access

• AWS S3 bucket and credentials (for large dataset storage)

🏃‍♂️ Usage

Large Dataset Handling

The get_endpoints tool automatically detects when a large dataset is returned (more than 10 endpoints) and:

1. Stores the complete dataset to your configured S3 bucket

2. Returns a summary showing only the first 5 endpoints

3. Provides a secure link to view the complete dataset (expires in 24 hours)

This prevents overwhelming responses while maintaining access to all data.

Python Script Execution

The execute_python_script tool allows you to dynamically execute Python code:

1. Saves the script to S3 using the /upload-text endpoint

2. Executes the script using the DataMaker runner via the /execute-python endpoint

3. Returns the execution output once the script completes

Usage Example:

# The tool accepts Python script code and a filename
execute_python_script(
  script="print('Hello from DataMaker!')",
  filename="hello.py"
)

This enables AI models to write and execute custom Python scripts for data processing, transformation, or any other computational tasks within the DataMaker environment.

Development Mode

Create a .env file in your project root. You can copy from env.example:

cp env.example .env

Then edit .env with your actual values:

DATAMAKER_URL="https://dev.datamaker.app"
DATAMAKER_API_KEY="your-datamaker-api-key"

# S3 Configuration (optional, for large dataset storage)
S3_BUCKET="your-s3-bucket-name"
S3_REGION="us-east-1"
S3_ACCESS_KEY_ID="your-aws-access-key"
S3_SECRET_ACCESS_KEY="your-aws-secret-key"

Run the server with the MCP Inspector for debugging:

pnpm dev

This will start the MCP server and launch the MCP Inspector interface at http://localhost:5173.

🔧 Available Scripts

• pnpm build - Build the TypeScript code

• pnpm dev - Start the development server with MCP Inspector

• pnpm changeset - Create a new changeset

• pnpm version - Update versions and changelogs

• pnpm release - Build and publish the package

🚢 Release Process

This project uses Changesets to manage versions, create changelogs, and publish to npm. Here's how to make a change:

1. Create a new branch

2. Make your changes

3. Create a changeset:

   pnpm changeset

4. Follow the prompts to describe your changes

5. Commit the changeset file along with your changes

6. Push to your branch

7. Create a PR on GitHub

The GitHub Actions workflow will automatically:

• Create a PR with version updates and changelog

• Publish to npm when the PR is merged

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

MIT License - See LICENSE for details.